Examining the impact of cognitive load on structure learning
6 agents, 12 issues
Method changes:
0
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0.25
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0.5
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0.75
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1
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Overall
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|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| high (N=52) |
low (N=37) |
high (N=41) |
low (N=54) |
high (N=49) |
low (N=51) |
high (N=41) |
low (N=47) |
high (N=45) |
low (N=41) |
high (N=228) |
low (N=230) |
|
| age | ||||||||||||
| Mean (SD) | 36.3 (11.7) | 38.4 (13.1) | 37.3 (12.8) | 39.4 (13.2) | 38.8 (13.9) | 37.0 (13.9) | 37.3 (9.68) | 36.1 (12.2) | 38.1 (12.7) | 36.9 (11.0) | 37.6 (12.2) | 37.6 (12.7) |
| Median [Min, Max] | 33.5 [19.0, 60.0] | 37.0 [22.0, 67.0] | 33.0 [18.0, 68.0] | 36.5 [18.0, 67.0] | 37.0 [18.0, 70.0] | 34.0 [19.0, 75.0] | 36.0 [21.0, 56.0] | 36.0 [19.0, 80.0] | 34.0 [19.0, 67.0] | 37.0 [20.0, 63.0] | 34.5 [18.0, 70.0] | 36.0 [18.0, 80.0] |
| race | ||||||||||||
| American Indian or Alaska Native | 3 (5.8%) | 0 (0%) | 0 (0%) | 1 (1.9%) | 0 (0%) | 0 (0%) | 1 (2.4%) | 0 (0%) | 0 (0%) | 1 (2.4%) | 4 (1.8%) | 2 (0.9%) |
| Asian | 4 (7.7%) | 5 (13.5%) | 4 (9.8%) | 4 (7.4%) | 3 (6.1%) | 4 (7.8%) | 4 (9.8%) | 2 (4.3%) | 6 (13.3%) | 3 (7.3%) | 21 (9.2%) | 18 (7.8%) |
| Black or African-American | 8 (15.4%) | 7 (18.9%) | 6 (14.6%) | 11 (20.4%) | 9 (18.4%) | 8 (15.7%) | 5 (12.2%) | 8 (17.0%) | 9 (20.0%) | 5 (12.2%) | 37 (16.2%) | 39 (17.0%) |
| Hispanic/Latinx | 2 (3.8%) | 3 (8.1%) | 5 (12.2%) | 9 (16.7%) | 5 (10.2%) | 4 (7.8%) | 4 (9.8%) | 4 (8.5%) | 3 (6.7%) | 3 (7.3%) | 19 (8.3%) | 23 (10.0%) |
| Native Hawaiian or Other Pacific Islander | 1 (1.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 0 (0%) |
| White | 34 (65.4%) | 22 (59.5%) | 26 (63.4%) | 29 (53.7%) | 30 (61.2%) | 35 (68.6%) | 27 (65.9%) | 32 (68.1%) | 27 (60.0%) | 28 (68.3%) | 144 (63.2%) | 146 (63.5%) |
| Other | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (4.1%) | 0 (0%) | 0 (0%) | 1 (2.1%) | 0 (0%) | 1 (2.4%) | 2 (0.9%) | 2 (0.9%) |
| gender | ||||||||||||
| Man | 23 (44.2%) | 15 (40.5%) | 18 (43.9%) | 21 (38.9%) | 20 (40.8%) | 27 (52.9%) | 19 (46.3%) | 22 (46.8%) | 16 (35.6%) | 12 (29.3%) | 96 (42.1%) | 97 (42.2%) |
| Non-binary | 2 (3.8%) | 1 (2.7%) | 1 (2.4%) | 2 (3.7%) | 1 (2.0%) | 0 (0%) | 1 (2.4%) | 0 (0%) | 1 (2.2%) | 0 (0%) | 6 (2.6%) | 3 (1.3%) |
| Woman | 27 (51.9%) | 21 (56.8%) | 22 (53.7%) | 30 (55.6%) | 26 (53.1%) | 24 (47.1%) | 21 (51.2%) | 25 (53.2%) | 28 (62.2%) | 29 (70.7%) | 124 (54.4%) | 129 (56.1%) |
| Prefer not to answer | 0 (0%) | 0 (0%) | 0 (0%) | 1 (1.9%) | 2 (4.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (0.9%) | 1 (0.4%) |
| matrix_acc | ||||||||||||
| Mean (SD) | 0.750 (0.269) | 0.956 (0.0735) | 0.771 (0.259) | 0.896 (0.197) | 0.829 (0.165) | 0.914 (0.163) | 0.787 (0.190) | 0.955 (0.0800) | 0.739 (0.199) | 0.954 (0.0917) | 0.775 (0.221) | 0.932 (0.138) |
| Median [Min, Max] | 0.875 [0, 1.00] | 1.00 [0.750, 1.00] | 0.875 [0, 1.00] | 1.00 [0, 1.00] | 0.875 [0.250, 1.00] | 1.00 [0, 1.00] | 0.750 [0.375, 1.00] | 1.00 [0.625, 1.00] | 0.750 [0.125, 1.00] | 1.00 [0.625, 1.00] | 0.875 [0, 1.00] | 1.00 [0, 1.00] |
| as.factor(matrix_n_correct) | ||||||||||||
| 0 | 3 (5.8%) | 0 (0%) | 2 (4.9%) | 2 (3.7%) | 0 (0%) | 1 (2.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 (2.2%) | 3 (1.3%) |
| 1 | 0 (0%) | 0 (0%) | 1 (2.4%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (2.2%) | 0 (0%) | 2 (0.9%) | 0 (0%) |
| 2 | 1 (1.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (2.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (0.9%) | 0 (0%) |
| 3 | 3 (5.8%) | 0 (0%) | 1 (2.4%) | 0 (0%) | 0 (0%) | 0 (0%) | 4 (9.8%) | 0 (0%) | 2 (4.4%) | 0 (0%) | 10 (4.4%) | 0 (0%) |
| 4 | 1 (1.9%) | 0 (0%) | 2 (4.9%) | 0 (0%) | 3 (6.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 (11.1%) | 0 (0%) | 11 (4.8%) | 0 (0%) |
| 5 | 9 (17.3%) | 0 (0%) | 2 (4.9%) | 0 (0%) | 2 (4.1%) | 1 (2.0%) | 7 (17.1%) | 1 (2.1%) | 9 (20.0%) | 2 (4.9%) | 29 (12.7%) | 4 (1.7%) |
| 6 | 8 (15.4%) | 2 (5.4%) | 12 (29.3%) | 6 (11.1%) | 15 (30.6%) | 6 (11.8%) | 10 (24.4%) | 1 (2.1%) | 9 (20.0%) | 0 (0%) | 54 (23.7%) | 15 (6.5%) |
| 7 | 12 (23.1%) | 9 (24.3%) | 9 (22.0%) | 17 (31.5%) | 13 (26.5%) | 12 (23.5%) | 9 (22.0%) | 12 (25.5%) | 12 (26.7%) | 9 (22.0%) | 55 (24.1%) | 59 (25.7%) |
| 8 | 15 (28.8%) | 26 (70.3%) | 12 (29.3%) | 29 (53.7%) | 15 (30.6%) | 31 (60.8%) | 11 (26.8%) | 33 (70.2%) | 7 (15.6%) | 30 (73.2%) | 60 (26.3%) | 149 (64.8%) |
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: corrresp
Chisq Df Pr(>Chisq)
opinion_round 243.3307 1 < 2.2e-16 ***
Deviant_threshold 14.4594 4 0.005964 **
matrix_cond 1.1573 1 0.282032
opinion_round:Deviant_threshold 9.3619 4 0.052663 .
opinion_round:matrix_cond 0.7073 1 0.400331
Deviant_threshold:matrix_cond 7.5011 4 0.111660
opinion_round:Deviant_threshold:matrix_cond 4.8297 4 0.305225
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 1 opinion_round.trend SE df asymp.LCL asymp.UCL z.ratio p.value
overall 0.171 0.011 Inf 0.15 0.193 15.603 <.0001
Results are averaged over the levels of: Deviant_threshold, matrix_cond
Confidence level used: 0.95 $emmeans
Deviant_threshold emmean SE df asymp.LCL asymp.UCL z.ratio p.value
0 1.55 0.0958 Inf 1.360 1.74 16.155 <.0001
0.25 1.37 0.0917 Inf 1.189 1.55 14.936 <.0001
0.5 1.23 0.0878 Inf 1.056 1.40 13.982 <.0001
0.75 1.14 0.0932 Inf 0.954 1.32 12.196 <.0001
1 1.27 0.0949 Inf 1.082 1.45 13.358 <.0001
Results are averaged over the levels of: matrix_cond
Results are given on the logit (not the response) scale.
Confidence level used: 0.95
$contrasts
contrast estimate SE df asymp.LCL
Deviant_threshold0 - Deviant_threshold0.25 0.1789 0.132 Inf -0.1821
Deviant_threshold0 - Deviant_threshold0.5 0.3198 0.130 Inf -0.0342
Deviant_threshold0 - Deviant_threshold0.75 0.4117 0.133 Inf 0.0475
Deviant_threshold0 - Deviant_threshold1 0.2799 0.135 Inf -0.0876
Deviant_threshold0.25 - Deviant_threshold0.5 0.1409 0.127 Inf -0.2048
Deviant_threshold0.25 - Deviant_threshold0.75 0.2327 0.131 Inf -0.1233
Deviant_threshold0.25 - Deviant_threshold1 0.1010 0.132 Inf -0.2585
Deviant_threshold0.5 - Deviant_threshold0.75 0.0918 0.128 Inf -0.2571
Deviant_threshold0.5 - Deviant_threshold1 -0.0399 0.129 Inf -0.3924
Deviant_threshold0.75 - Deviant_threshold1 -0.1317 0.133 Inf -0.4943
asymp.UCL z.ratio p.value
0.540 1.352 0.6584
0.674 2.464 0.0989
0.776 3.084 0.0175
0.647 2.078 0.2296
0.487 1.112 0.8005
0.589 1.783 0.3834
0.460 0.766 0.9402
0.441 0.718 0.9525
0.313 -0.309 0.9980
0.231 -0.991 0.8595
Results are averaged over the levels of: matrix_cond
Results are given on the log odds ratio (not the response) scale.
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 5 estimates
P value adjustment: tukey method for comparing a family of 5 estimates $emmeans
matrix_cond emmean SE df asymp.LCL asymp.UCL z.ratio p.value
high 1.37 0.0588 Inf 1.25 1.48 23.222 <.0001
low 1.25 0.0587 Inf 1.14 1.37 21.355 <.0001
Results are averaged over the levels of: Deviant_threshold
Results are given on the logit (not the response) scale.
Confidence level used: 0.95
$contrasts
contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
high - low 0.113 0.0828 Inf -0.0495 0.275 1.363 0.1730
Results are averaged over the levels of: Deviant_threshold
Results are given on the log odds ratio (not the response) scale.
Confidence level used: 0.95 Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value
targetpair 1520 1520 1 458 5.5428
Deviant_threshold 70303 70303 1 458 256.4093
matrix_cond 9 9 1 458 0.0312
targetpair:Deviant_threshold 83118 83118 1 458 303.1459
targetpair:matrix_cond 839 839 1 458 3.0601
Deviant_threshold:matrix_cond 14 14 1 458 0.0511
targetpair:Deviant_threshold:matrix_cond 1593 1593 1 458 5.8116
Pr(>F)
targetpair 0.01898 *
Deviant_threshold < 2e-16 ***
matrix_cond 0.85992
targetpair:Deviant_threshold < 2e-16 ***
targetpair:matrix_cond 0.08091 .
Deviant_threshold:matrix_cond 0.82134
targetpair:Deviant_threshold:matrix_cond 0.01631 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1$emtrends
targetpair Deviant_threshold.trend SE df lower.CL upper.CL t.ratio p.value
DN -57.668 2.60 458 -62.78 -52.55 -22.155 <.0001
NN -0.899 2.29 458 -5.39 3.59 -0.393 0.6945
Results are averaged over the levels of: matrix_cond
Degrees-of-freedom method: satterthwaite
Confidence level used: 0.95
$contrasts
contrast estimate SE df lower.CL upper.CL t.ratio p.value
DN - NN -56.8 3.26 458 -63.2 -50.4 -17.411 <.0001
Results are averaged over the levels of: matrix_cond
Degrees-of-freedom method: satterthwaite
Confidence level used: 0.95 # A tibble: 4 × 14
# Groups: matrix_cond [2]
matrix_cond id term estimate std.error statistic p.value conf.low
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 high below_.5 Deviant_th… -9.01 6.81 -1.32 0.188 -22.5
2 high above_.5 Deviant_th… 5.47 6.75 0.811 0.419 -7.88
3 low below_.5 Deviant_th… -19.0 7.21 -2.64 0.00925 -33.3
4 low above_.5 Deviant_th… 22.4 6.69 3.34 0.00107 9.13
# ℹ 6 more variables: conf.high <dbl>, r.squared <dbl>, adj.r.squared <dbl>,
# df <dbl>, df.residual <int>, nobs <int># A tibble: 4 × 14
# Groups: matrix_cond [2]
matrix_cond id term estimate std.error statistic p.value conf.low
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 high below_.5 Deviant_th… -23.5 10.5 -2.24 0.0266 -44.2
2 high above_.5 Deviant_th… -8.35 10.6 -0.786 0.433 -29.4
3 low below_.5 Deviant_th… -5.17 12.2 -0.424 0.672 -29.3
4 low above_.5 Deviant_th… -14.2 11.8 -1.21 0.228 -37.5
# ℹ 6 more variables: conf.high <dbl>, r.squared <dbl>, adj.r.squared <dbl>,
# df <dbl>, df.residual <int>, nobs <int>Analysis of Variance Table
Response: confidence
Df Sum Sq Mean Sq F value Pr(>F)
deviance 4 9049 2262.16 3.0597 0.01661 *
matrix_cond 1 700 700.21 0.9471 0.33100
deviance:matrix_cond 4 898 224.47 0.3036 0.87555
Residuals 448 331230 739.35
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1$emmeans
deviance emmean SE df lower.CL upper.CL t.ratio p.value
0 61.3 2.92 448 55.6 67.1 20.967 <.0001
0.25 52.6 2.82 448 47.1 58.1 18.677 <.0001
0.5 53.8 2.72 448 48.5 59.2 19.798 <.0001
0.75 50.2 2.91 448 44.5 55.9 17.284 <.0001
1 48.3 2.94 448 42.5 54.0 16.447 <.0001
Results are averaged over the levels of: matrix_cond
Confidence level used: 0.95
$contrasts
contrast estimate SE df lower.CL upper.CL t.ratio
deviance0 - deviance0.25 8.71 4.06 448 -2.410 19.83 2.145
deviance0 - deviance0.5 7.46 3.99 448 -3.473 18.40 1.869
deviance0 - deviance0.75 11.09 4.12 448 -0.196 22.38 2.691
deviance0 - deviance1 13.03 4.14 448 1.685 24.38 3.146
deviance0.25 - deviance0.5 -1.25 3.92 448 -11.968 9.48 -0.318
deviance0.25 - deviance0.75 2.38 4.05 448 -8.698 13.47 0.589
deviance0.25 - deviance1 4.32 4.07 448 -6.818 15.46 1.063
deviance0.5 - deviance0.75 3.63 3.98 448 -7.270 14.53 0.912
deviance0.5 - deviance1 5.57 4.00 448 -5.391 16.53 1.392
deviance0.75 - deviance1 1.94 4.13 448 -9.372 13.25 0.470
p.value
0.2029
0.3356
0.0568
0.0151
0.9978
0.9766
0.8254
0.8922
0.6334
0.9900
Results are averaged over the levels of: matrix_cond
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 5 estimates
P value adjustment: tukey method for comparing a family of 5 estimates | 0 (N=89) |
0.25 (N=95) |
0.5 (N=100) |
0.75 (N=88) |
1 (N=86) |
Overall (N=458) |
|
|---|---|---|---|---|---|---|
| pred_maj | ||||||
| Yes | 9 (10.1%) | 18 (18.9%) | 12 (12.0%) | 16 (18.2%) | 19 (22.1%) | 74 (16.2%) |
| No | 80 (89.9%) | 77 (81.1%) | 88 (88.0%) | 72 (81.8%) | 67 (77.9%) | 384 (83.8%) |
# A tibble: 4 × 14
# Groups: pred_maj [2]
pred_maj id term estimate std.error statistic p.value conf.low conf.high
<lgl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 FALSE below_… Devi… -20.7 8.14 -2.55 0.0115 -36.8 -4.70
2 FALSE above_… Devi… -4.17 8.71 -0.479 0.632 -21.3 13.0
3 TRUE below_… Devi… 38.4 24.9 1.54 0.131 -12.0 88.8
4 TRUE above_… Devi… -40.7 19.0 -2.14 0.0379 -79.1 -2.38
# ℹ 5 more variables: r.squared <dbl>, adj.r.squared <dbl>, df <dbl>,
# df.residual <int>, nobs <int>Analysis of Variance Table
Response: confidence
Df Sum Sq Mean Sq F value Pr(>F)
deviance 4 9049 2262.2 3.1999 0.0131279 *
pred_maj 1 9735 9735.5 13.7713 0.0002324 ***
deviance:pred_maj 4 6384 1595.9 2.2574 0.0621099 .
Residuals 448 316709 706.9
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1| 0 (N=89) |
0.25 (N=95) |
0.5 (N=100) |
0.75 (N=88) |
1 (N=86) |
Overall (N=458) |
|
|---|---|---|---|---|---|---|
| pns_med | ||||||
| High | 37 (41.6%) | 47 (49.5%) | 42 (42.0%) | 44 (50.0%) | 37 (43.0%) | 207 (45.2%) |
| Low | 51 (57.3%) | 48 (50.5%) | 58 (58.0%) | 44 (50.0%) | 49 (57.0%) | 250 (54.6%) |
| Missing | 1 (1.1%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.2%) |
# A tibble: 4 × 14
# Groups: pns_med [2]
pns_med id term estimate std.error statistic p.value conf.low conf.high
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 High below_.5 Devi… -9.68 13.6 -0.714 0.476 -36.5 17.1
2 High above_.5 Devi… -0.121 13.5 -0.00893 0.993 -26.8 26.6
3 Low below_.5 Devi… -18.8 9.59 -1.96 0.0520 -37.7 0.165
4 Low above_.5 Devi… -20.1 9.39 -2.14 0.0341 -38.6 -1.53
# ℹ 5 more variables: r.squared <dbl>, adj.r.squared <dbl>, df <dbl>,
# df.residual <int>, nobs <int>Analysis of Variance Table
Response: confidence
Df Sum Sq Mean Sq F value Pr(>F)
deviance 4 9170 2292.56 3.1300 0.01477 *
pns_med 1 6 6.18 0.0084 0.92688
deviance:pns_med 4 5287 1321.71 1.8045 0.12685
Residuals 447 327402 732.44
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1| 0 (N=458) |
1 (N=458) |
2 (N=458) |
3 (N=458) |
4 (N=458) |
5 (N=458) |
6 (N=458) |
7 (N=458) |
Overall (N=3664) |
|
|---|---|---|---|---|---|---|---|---|---|
| trialnum | |||||||||
| 0 | 52 (11.4%) | 68 (14.8%) | 68 (14.8%) | 54 (11.8%) | 49 (10.7%) | 77 (16.8%) | 56 (12.2%) | 56 (12.2%) | 480 (13.1%) |
| 1 | 47 (10.3%) | 53 (11.6%) | 42 (9.2%) | 66 (14.4%) | 57 (12.4%) | 67 (14.6%) | 58 (12.7%) | 56 (12.2%) | 446 (12.2%) |
| 2 | 59 (12.9%) | 61 (13.3%) | 56 (12.2%) | 51 (11.1%) | 65 (14.2%) | 45 (9.8%) | 63 (13.8%) | 51 (11.1%) | 451 (12.3%) |
| 3 | 63 (13.8%) | 59 (12.9%) | 58 (12.7%) | 53 (11.6%) | 53 (11.6%) | 49 (10.7%) | 51 (11.1%) | 45 (9.8%) | 431 (11.8%) |
| 4 | 64 (14.0%) | 46 (10.0%) | 61 (13.3%) | 69 (15.1%) | 66 (14.4%) | 59 (12.9%) | 51 (11.1%) | 68 (14.8%) | 484 (13.2%) |
| 5 | 47 (10.3%) | 62 (13.5%) | 61 (13.3%) | 59 (12.9%) | 59 (12.9%) | 62 (13.5%) | 60 (13.1%) | 65 (14.2%) | 475 (13.0%) |
| 6 | 66 (14.4%) | 52 (11.4%) | 54 (11.8%) | 46 (10.0%) | 59 (12.9%) | 60 (13.1%) | 63 (13.8%) | 59 (12.9%) | 459 (12.5%) |
| 7 | 60 (13.1%) | 57 (12.4%) | 58 (12.7%) | 60 (13.1%) | 50 (10.9%) | 39 (8.5%) | 56 (12.2%) | 58 (12.7%) | 438 (12.0%) |